AeroLat: Channel-Aware Latent Space Semantic Communication for Decentralized UAV Swarms
cs.NI, cs.AI
Submitted: 2026-09-15
Updated: 2026-09-15
License: http://creativecommons.org/licenses/by-nc-nd/4.0/
The gist: Communication in latent space offers an intriguing alternative to symbolic messages for decentralized autonomous Unmanned Aerial Vehicle (UAV) swarms operating over bandwidth-constrained,
Terminology
Abstract
Communication in latent space offers an intriguing alternative to symbolic messages for decentralized autonomous Unmanned Aerial Vehicle (UAV) swarms operating over bandwidth-constrained, time-varying wireless links. However, when homogeneous frozen models are prompted with discretized perceptual inputs, their broadcast states collapse toward the shared prompt template. In view of this, we propose AeroLat, a channel-aware latent semantic communication framework that uses evidence injection. The resulting latent states are then passed through an explicit communication model that encompasses bandwidth-limited serialization, additive noise and information staleness, which facilitates a joint assessment of communication fidelity and swarm-level coordination. Across multi-seed simulations, AeroLat provably remains resilient to codec choice, faults and increasing swarm size. It consistently reproduces the latent-swarm anomaly, while no-whitening controls recover the collapse. In particular, AeroLat is capable of reducing false similarity by 97.5%.
Sources
- Latent Collaboration in Multi-Agent Systems
- Representational Collapse in Multi-Agent LLM Committees: Measurement and Diversity-Aware Consensus
- Relative representations enable zero-shot latent space communication
- Multimodal Chain of Continuous Thought for Latent-Space Reasoning in Vision-Language Models
- Spatial Semantic Communication: When Semantic Transmission Meets Index Modulation
- Qwen2.5-Coder Technical Report
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